{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>api</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>interval</th>\n",
       "      <th>created_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019162542</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:00:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162644</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>162742</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
       "      <td>169.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:02:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>162808</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>9</td>\n",
       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:03:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>162943</td>\n",
       "      <td>/front-api/bill/create</td>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "      <td>60</td>\n",
       "      <td>2018-11-01 00:04:07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id                     api  count  res_time_sum  res_time_min  \\\n",
       "0  2019162542  /front-api/bill/create      8       1057.31         88.75   \n",
       "1      162644  /front-api/bill/create      5        749.12        103.79   \n",
       "2      162742  /front-api/bill/create      5        845.84        136.31   \n",
       "3      162808  /front-api/bill/create      9       1305.52         90.12   \n",
       "4      162943  /front-api/bill/create      3        568.89        138.45   \n",
       "\n",
       "   res_time_max  res_time_avg  interval           created_at  \n",
       "0        177.72         132.0        60  2018-11-01 00:00:07  \n",
       "1        240.38         149.0        60  2018-11-01 00:01:07  \n",
       "2        225.73         169.0        60  2018-11-01 00:02:07  \n",
       "3        196.61         145.0        60  2018-11-01 00:03:07  \n",
       "4        232.02         189.0        60  2018-11-01 00:04:07  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('D:/homework7/log.txt', header = None, sep = '\\t')\n",
    "df.columns = ['id','api','count','res_time_sum','res_time_min','res_time_max','res_time_avg','interval','created_at']\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                int64\n",
       "api              object\n",
       "count             int64\n",
       "res_time_sum    float64\n",
       "res_time_min    float64\n",
       "res_time_max    float64\n",
       "res_time_avg    float64\n",
       "interval          int64\n",
       "created_at       object\n",
       "dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 179496 entries, 0 to 179495\n",
      "Data columns (total 9 columns):\n",
      " #   Column        Non-Null Count   Dtype  \n",
      "---  ------        --------------   -----  \n",
      " 0   id            179496 non-null  int64  \n",
      " 1   api           179496 non-null  object \n",
      " 2   count         179496 non-null  int64  \n",
      " 3   res_time_sum  179496 non-null  float64\n",
      " 4   res_time_min  179496 non-null  float64\n",
      " 5   res_time_max  179496 non-null  float64\n",
      " 6   res_time_avg  179496 non-null  float64\n",
      " 7   interval      179496 non-null  int64  \n",
      " 8   created_at    179496 non-null  object \n",
      "dtypes: float64(4), int64(3), object(2)\n",
      "memory usage: 12.3+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                     179496\n",
       "unique                         1\n",
       "top       /front-api/bill/create\n",
       "freq                      179496\n",
       "Name: api, dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['api'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    179496.0\n",
       "mean         60.0\n",
       "std           0.0\n",
       "min          60.0\n",
       "25%          60.0\n",
       "50%          60.0\n",
       "75%          60.0\n",
       "max          60.0\n",
       "Name: interval, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['interval'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>count</th>\n",
       "      <th>res_time_sum</th>\n",
       "      <th>res_time_min</th>\n",
       "      <th>res_time_max</th>\n",
       "      <th>res_time_avg</th>\n",
       "      <th>created_at</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2019162542</td>\n",
       "      <td>8</td>\n",
       "      <td>1057.31</td>\n",
       "      <td>88.75</td>\n",
       "      <td>177.72</td>\n",
       "      <td>132.0</td>\n",
       "      <td>2018-11-01 00:00:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>162644</td>\n",
       "      <td>5</td>\n",
       "      <td>749.12</td>\n",
       "      <td>103.79</td>\n",
       "      <td>240.38</td>\n",
       "      <td>149.0</td>\n",
       "      <td>2018-11-01 00:01:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>162742</td>\n",
       "      <td>5</td>\n",
       "      <td>845.84</td>\n",
       "      <td>136.31</td>\n",
       "      <td>225.73</td>\n",
       "      <td>169.0</td>\n",
       "      <td>2018-11-01 00:02:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>162808</td>\n",
       "      <td>9</td>\n",
       "      <td>1305.52</td>\n",
       "      <td>90.12</td>\n",
       "      <td>196.61</td>\n",
       "      <td>145.0</td>\n",
       "      <td>2018-11-01 00:03:07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>162943</td>\n",
       "      <td>3</td>\n",
       "      <td>568.89</td>\n",
       "      <td>138.45</td>\n",
       "      <td>232.02</td>\n",
       "      <td>189.0</td>\n",
       "      <td>2018-11-01 00:04:07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id  count  res_time_sum  res_time_min  res_time_max  res_time_avg  \\\n",
       "0  2019162542      8       1057.31         88.75        177.72         132.0   \n",
       "1      162644      5        749.12        103.79        240.38         149.0   \n",
       "2      162742      5        845.84        136.31        225.73         169.0   \n",
       "3      162808      9       1305.52         90.12        196.61         145.0   \n",
       "4      162943      3        568.89        138.45        232.02         189.0   \n",
       "\n",
       "            created_at  \n",
       "0  2018-11-01 00:00:07  \n",
       "1  2018-11-01 00:01:07  \n",
       "2  2018-11-01 00:02:07  \n",
       "3  2018-11-01 00:03:07  \n",
       "4  2018-11-01 00:04:07  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.drop(['api','interval'],axis = 1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count                  179496\n",
       "unique                 179496\n",
       "top       2019-02-11 11:45:06\n",
       "freq                        1\n",
       "Name: created_at, dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['created_at'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.index = df.created_at"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['2018-11-01 00:00:07', '2018-11-01 00:01:07', '2018-11-01 00:02:07',\n",
       "       '2018-11-01 00:03:07', '2018-11-01 00:04:07', '2018-11-01 00:05:07',\n",
       "       '2018-11-01 00:06:07', '2018-11-01 00:07:07', '2018-11-01 00:08:07',\n",
       "       '2018-11-01 00:09:07',\n",
       "       ...\n",
       "       '2019-05-30 23:01:21', '2019-05-30 23:02:21', '2019-05-30 23:03:21',\n",
       "       '2019-05-30 23:04:21', '2019-05-30 23:05:21', '2019-05-30 23:06:21',\n",
       "       '2019-05-30 23:07:21', '2019-05-30 23:08:21', '2019-05-30 23:09:21',\n",
       "       '2019-05-30 23:10:21'],\n",
       "      dtype='object', name='created_at', length=179496)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.index = pd.to_datetime(df.created_at)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2018-11-01 00:00:07', '2018-11-01 00:01:07',\n",
       "               '2018-11-01 00:02:07', '2018-11-01 00:03:07',\n",
       "               '2018-11-01 00:04:07', '2018-11-01 00:05:07',\n",
       "               '2018-11-01 00:06:07', '2018-11-01 00:07:07',\n",
       "               '2018-11-01 00:08:07', '2018-11-01 00:09:07',\n",
       "               ...\n",
       "               '2019-05-30 23:01:21', '2019-05-30 23:02:21',\n",
       "               '2019-05-30 23:03:21', '2019-05-30 23:04:21',\n",
       "               '2019-05-30 23:05:21', '2019-05-30 23:06:21',\n",
       "               '2019-05-30 23:07:21', '2019-05-30 23:08:21',\n",
       "               '2019-05-30 23:09:21', '2019-05-30 23:10:21'],\n",
       "              dtype='datetime64[ns]', name='created_at', length=179496, freq=None)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['2019-05-30']['count'].plot()\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "date = df['2019-5-1'].resample('20T').mean()\n",
    "date[['res_time_min','res_time_max','res_time_avg']].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['2019-5-11':'2019-5-20']['count'].plot()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['weekday'] = df.index.weekday\n",
    "df['weekend'] = df['weekday'].isin({5,6})\n",
    "df.groupby(['weekend',df.index.hour])['count'].mean().unstack(level = 0).plot()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
